Localization of mobile robots has been an active area of research since progress in robotics allowed for semi-autonomous and autonomous robots. A moving robot must have a method of at least estimating its position in space to continue motion with a purpose. To estimate its position, the robot must use some sort of a map on which it can place itself. Such a map can be preloaded on a robot, or it can be obtained by the robot itself. In the latter case, the map can be obtained simultaneously with the robot localization, or it can first be developed to be used later as part of localization. The modern framework of localization comprises a coordinate system with respect to which the localization is done and a pose, which is a combination of position and orientation. Localization can be done with respect to an absolute coordinate system (such as GPS coordinates) or a relative coordinate system (such as localization with respect to some known location and/or object). The coordinate system can be chosen arbitrarily, as long as it is consistent and can be converted to some standard coordinate system (such as WGS84) if needed.
Multiple sensor readings can contribute to pose calculation—it can be determined using GPS receivers, Lidar (light radar) sensors, cameras, odometers, gyroscopes, accelerometers, magnetometers, time of flight cameras and radar sensors. There is an important distinction to be made in the context of localization: it can be done based on an existing map, or it can be done simultaneously with mapping. The latter is called SLAM (Simultaneous Localization and Mapping) and is the preferred approach when localization is performed while exploring previously unknown surroundings. If a map is already available, the task becomes easier. For example, localization in indoor environments (such as an apartment or a hospital) or structured outdoor environments (such as a factory complex) is easier, since a detailed map is readily available, and it is unlikely to change significantly in the short term. Localization outdoors, in unstructured environments (such as cities, suburbs and/or villages) is a greater challenge. First, publicly available maps are not precise enough for autonomous motion by the robot. Second, even when the maps exist, they are likely to get outdated very fast, as new obstacles appear and old pathways disappear. Localization outdoors can be done with the help of a positioning system such as GPS (Global Positioning System), GLONASS (Global Navigation Satellite System) or Galileo. However, the precision of these systems (available for public use) is on the order of 1-10 meters. This would not be enough for localization used for autonomous robotic navigation.
Existing solutions to the localization problem strongly depend on the intended application of the object to be localized.
U.S. Pat. No. 8,717,545 B2 discloses a system using range and Doppler velocity measurements from a lidar system and images from a video system to estimate a six degree-of-freedom trajectory of a target. Once the motion aspects of the target are estimated, a three-dimensional image of the target may be generated.
U.S. Pat. No. 7,015,831 B2 discloses a method and apparatus that use a visual sensor and dead reckoning sensors to process Simultaneous Localization and Mapping (SLAM). Advantageously, such visual techniques can be used to autonomously generate and update a map. Unlike with laser rangefinders, the visual techniques are economically practical in a wide range of applications and can be used in relatively dynamic environments, such as environments in which people move.
The disclosed method can be implemented in an automatic vacuum cleaner operating in an indoor environment.
U.S. Pat. No. 8,305,430 B2 discloses a visual odometry system and method for a fixed or known calibration of an arbitrary number of cameras in monocular configuration. Images collected from each of the cameras in this distributed aperture system have negligible or absolutely no overlap.
The system uses multiple cameras, but generates pose hypothesis in each camera separately before comparing and refining them.
Schindler, et al. (in 3D Data Processing, Visualization, and Transmission, Third International Symposium on, pp. 846-853. IEEE, 2006) discusses a novel method for recovering the 3D-line structure of a scene from multiple widely separated views. In this approach, 2D-lines are automatically detected in images with the assistance of an EM-based vanishing point estimation method which assumes the existence of edges along mutually orthogonal vanishing directions. 3D reconstruction results for urban scenes based on manually established feature correspondences across images are presented.
Murillo, A. C., et al. (Robotics and Autonomous Systems 55, no. 5 (2007): 372-382) proposes a new vision-based method for global robot localization using an omnidirectional camera. Topological and metric localization information are combined in an efficient, hierarchical process, with each step being more complex and accurate than the previous one but evaluating fewer images.